Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations180519
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory127.2 MiB
Average record size in memory738.6 B

Variable types

DateTime2
Categorical8
Numeric8
Text4

Alerts

delivery_status is highly overall correlated with late_delivery_risk and 2 other fieldsHigh correlation
discount is highly overall correlated with discount_rate and 1 other fieldsHigh correlation
discount_rate is highly overall correlated with discountHigh correlation
late_delivery_risk is highly overall correlated with delivery_status and 1 other fieldsHigh correlation
order_status is highly overall correlated with delivery_statusHigh correlation
product_price is highly overall correlated with sales_actual and 1 other fieldsHigh correlation
sales_actual is highly overall correlated with product_price and 1 other fieldsHigh correlation
sales_origin is highly overall correlated with discount and 2 other fieldsHigh correlation
shipping_days_actual is highly overall correlated with delivery_status and 3 other fieldsHigh correlation
shipping_days_scheduled is highly overall correlated with shipping_days_actual and 1 other fieldsHigh correlation
shipping_mode is highly overall correlated with shipping_days_actual and 1 other fieldsHigh correlation
shipping_days_actual has 5080 (2.8%) zeros Zeros
discount has 10028 (5.6%) zeros Zeros
discount_rate has 10028 (5.6%) zeros Zeros

Reproduction

Analysis started2025-05-05 16:17:38.060093
Analysis finished2025-05-05 16:18:09.850241
Duration31.79 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct1127
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Minimum2015-01-01 00:00:00
Maximum2018-01-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-05T23:18:10.112945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:10.457663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1131
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Minimum2015-01-03 00:00:00
Maximum2018-02-06 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-05T23:18:10.742718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:11.061148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

shipping_mode
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
Standard Class
107752 
Second Class
35216 
First Class
27814 
Same Day
 
9737

Length

Max length14
Median length14
Mean length12.823969
Min length8

Characters and Unicode

Total characters2314970
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStandard Class
2nd rowStandard Class
3rd rowStandard Class
4th rowStandard Class
5th rowStandard Class

Common Values

ValueCountFrequency (%)
Standard Class 107752
59.7%
Second Class 35216
 
19.5%
First Class 27814
 
15.4%
Same Day 9737
 
5.4%

Length

2025-05-05T23:18:11.327736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-05T23:18:11.795782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
class 170782
47.3%
standard 107752
29.8%
second 35216
 
9.8%
first 27814
 
7.7%
same 9737
 
2.7%
day 9737
 
2.7%

Most occurring characters

ValueCountFrequency (%)
a 405760
17.5%
s 369378
16.0%
d 250720
10.8%
180519
7.8%
l 170782
7.4%
C 170782
7.4%
S 152705
 
6.6%
n 142968
 
6.2%
r 135566
 
5.9%
t 135566
 
5.9%
Other values (8) 200224
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2314970
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 405760
17.5%
s 369378
16.0%
d 250720
10.8%
180519
7.8%
l 170782
7.4%
C 170782
7.4%
S 152705
 
6.6%
n 142968
 
6.2%
r 135566
 
5.9%
t 135566
 
5.9%
Other values (8) 200224
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2314970
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 405760
17.5%
s 369378
16.0%
d 250720
10.8%
180519
7.8%
l 170782
7.4%
C 170782
7.4%
S 152705
 
6.6%
n 142968
 
6.2%
r 135566
 
5.9%
t 135566
 
5.9%
Other values (8) 200224
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2314970
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 405760
17.5%
s 369378
16.0%
d 250720
10.8%
180519
7.8%
l 170782
7.4%
C 170782
7.4%
S 152705
 
6.6%
n 142968
 
6.2%
r 135566
 
5.9%
t 135566
 
5.9%
Other values (8) 200224
8.6%

delivery_status
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
Late delivery
98977 
Advance shipping
41592 
Shipping on time
32196 
Shipping canceled
 
7754

Length

Max length17
Median length13
Mean length14.39808
Min length13

Characters and Unicode

Total characters2599127
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdvance shipping
2nd rowLate delivery
3rd rowShipping on time
4th rowAdvance shipping
5th rowAdvance shipping

Common Values

ValueCountFrequency (%)
Late delivery 98977
54.8%
Advance shipping 41592
23.0%
Shipping on time 32196
 
17.8%
Shipping canceled 7754
 
4.3%

Length

2025-05-05T23:18:12.194043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-05T23:18:12.443580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
late 98977
25.2%
delivery 98977
25.2%
shipping 81542
20.7%
advance 41592
10.6%
on 32196
 
8.2%
time 32196
 
8.2%
canceled 7754
 
2.0%

Most occurring characters

ValueCountFrequency (%)
e 386227
14.9%
i 294257
11.3%
212715
 
8.2%
n 163084
 
6.3%
p 163084
 
6.3%
d 148323
 
5.7%
a 148323
 
5.7%
v 140569
 
5.4%
t 131173
 
5.0%
l 106731
 
4.1%
Other values (11) 704641
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2599127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 386227
14.9%
i 294257
11.3%
212715
 
8.2%
n 163084
 
6.3%
p 163084
 
6.3%
d 148323
 
5.7%
a 148323
 
5.7%
v 140569
 
5.4%
t 131173
 
5.0%
l 106731
 
4.1%
Other values (11) 704641
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2599127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 386227
14.9%
i 294257
11.3%
212715
 
8.2%
n 163084
 
6.3%
p 163084
 
6.3%
d 148323
 
5.7%
a 148323
 
5.7%
v 140569
 
5.4%
t 131173
 
5.0%
l 106731
 
4.1%
Other values (11) 704641
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2599127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 386227
14.9%
i 294257
11.3%
212715
 
8.2%
n 163084
 
6.3%
p 163084
 
6.3%
d 148323
 
5.7%
a 148323
 
5.7%
v 140569
 
5.4%
t 131173
 
5.0%
l 106731
 
4.1%
Other values (11) 704641
27.1%

shipping_days_actual
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.497654
Minimum0
Maximum6
Zeros5080
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-05-05T23:18:12.667887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6237218
Coefficient of variation (CV)0.46423169
Kurtosis-1.0079136
Mean3.497654
Median Absolute Deviation (MAD)1
Skewness0.084771273
Sum631393
Variance2.6364726
MonotonicityNot monotonic
2025-05-05T23:18:12.874306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 56618
31.4%
3 28765
15.9%
6 28723
15.9%
4 28513
15.8%
5 28163
15.6%
0 5080
 
2.8%
1 4657
 
2.6%
ValueCountFrequency (%)
0 5080
 
2.8%
1 4657
 
2.6%
2 56618
31.4%
3 28765
15.9%
4 28513
15.8%
5 28163
15.6%
6 28723
15.9%
ValueCountFrequency (%)
6 28723
15.9%
5 28163
15.6%
4 28513
15.8%
3 28765
15.9%
2 56618
31.4%
1 4657
 
2.6%
0 5080
 
2.8%

shipping_days_scheduled
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
4
107752 
2
35216 
1
27814 
0
 
9737

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180519
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 107752
59.7%
2 35216
 
19.5%
1 27814
 
15.4%
0 9737
 
5.4%

Length

2025-05-05T23:18:13.187930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-05T23:18:13.468048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 107752
59.7%
2 35216
 
19.5%
1 27814
 
15.4%
0 9737
 
5.4%

Most occurring characters

ValueCountFrequency (%)
4 107752
59.7%
2 35216
 
19.5%
1 27814
 
15.4%
0 9737
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180519
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 107752
59.7%
2 35216
 
19.5%
1 27814
 
15.4%
0 9737
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180519
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 107752
59.7%
2 35216
 
19.5%
1 27814
 
15.4%
0 9737
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180519
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 107752
59.7%
2 35216
 
19.5%
1 27814
 
15.4%
0 9737
 
5.4%

late_delivery_risk
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
1
98977 
0
81542 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 98977
54.8%
0 81542
45.2%

Length

2025-05-05T23:18:13.766869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-05T23:18:14.070435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 98977
54.8%
0 81542
45.2%

Most occurring characters

ValueCountFrequency (%)
1 98977
54.8%
0 81542
45.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180519
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 98977
54.8%
0 81542
45.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180519
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 98977
54.8%
0 81542
45.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180519
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 98977
54.8%
0 81542
45.2%

sales_origin
Real number (ℝ)

High correlation 

Distinct193
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203.7721
Minimum9.99
Maximum1999.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-05-05T23:18:14.407492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.99
5-th percentile49.98
Q1119.98
median199.92
Q3299.95001
95-th percentile399.98001
Maximum1999.99
Range1990
Interquartile range (IQR)179.97001

Descriptive statistics

Standard deviation132.27308
Coefficient of variation (CV)0.64912262
Kurtosis23.936562
Mean203.7721
Median Absolute Deviation (MAD)79.94
Skewness2.8842491
Sum36784735
Variance17496.167
MonotonicityNot monotonic
2025-05-05T23:18:14.835346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.99001 22372
 
12.4%
399.98001 17325
 
9.6%
199.99001 15622
 
8.7%
299.98001 13729
 
7.6%
179.97 5016
 
2.8%
299.95001 4988
 
2.8%
119.98 4968
 
2.8%
239.96001 4955
 
2.7%
59.99 4893
 
2.7%
50 4432
 
2.5%
Other values (183) 82219
45.5%
ValueCountFrequency (%)
9.99 56
 
< 0.1%
11.29 271
0.2%
11.54 529
0.3%
14.99 124
 
0.1%
15.99 118
 
0.1%
17.99 62
 
< 0.1%
19.98 54
 
< 0.1%
19.99 176
 
0.1%
21.99 51
 
< 0.1%
22 64
 
< 0.1%
ValueCountFrequency (%)
1999.98999 15
 
< 0.1%
1500 442
 
0.2%
999.98999 10
 
< 0.1%
599.98999 21
 
< 0.1%
532.58002 484
 
0.3%
500 15
 
< 0.1%
499.95001 2510
1.4%
499.75 12
 
< 0.1%
495 14
 
< 0.1%
474.95001 15
 
< 0.1%

sales_actual
Real number (ℝ)

High correlation 

Distinct2927
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.10761
Minimum7.49
Maximum1939.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-05-05T23:18:15.259188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.49
5-th percentile41.5
Q1104.38
median163.99001
Q3247.39999
95-th percentile383.98001
Maximum1939.99
Range1932.5
Interquartile range (IQR)143.01999

Descriptive statistics

Standard deviation120.04367
Coefficient of variation (CV)0.65559083
Kurtosis23.920362
Mean183.10761
Median Absolute Deviation (MAD)67.00001
Skewness2.8884461
Sum33054402
Variance14410.483
MonotonicityNot monotonic
2025-05-05T23:18:15.676501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122.84 1264
 
0.7%
109.19 1247
 
0.7%
124.79 1243
 
0.7%
129.99001 1243
 
0.7%
116.99 1243
 
0.7%
123.49 1243
 
0.7%
120.89 1243
 
0.7%
127.39 1243
 
0.7%
97.49 1243
 
0.7%
118.29 1243
 
0.7%
Other values (2917) 168064
93.1%
ValueCountFrequency (%)
7.49 3
 
< 0.1%
7.99 3
 
< 0.1%
8.19 3
 
< 0.1%
8.29 3
 
< 0.1%
8.39 3
 
< 0.1%
8.47 15
< 0.1%
8.49 3
 
< 0.1%
8.66 29
< 0.1%
8.69 3
 
< 0.1%
8.79 3
 
< 0.1%
ValueCountFrequency (%)
1939.98999 1
< 0.1%
1919.98999 1
< 0.1%
1899.98999 1
< 0.1%
1889.98999 1
< 0.1%
1859.98999 1
< 0.1%
1819.98999 1
< 0.1%
1799.98999 1
< 0.1%
1759.98999 1
< 0.1%
1739.98999 1
< 0.1%
1699.98999 1
< 0.1%

profit_per_order
Real number (ℝ)

Distinct21998
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.974989
Minimum-4274.98
Maximum911.8
Zeros1177
Zeros (%)0.7%
Negative33784
Negative (%)18.7%
Memory size1.4 MiB
2025-05-05T23:18:16.007074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4274.98
5-th percentile-139.251
Q17
median31.52
Q364.8
95-th percentile132.29
Maximum911.8
Range5186.78
Interquartile range (IQR)57.8

Descriptive statistics

Standard deviation104.43353
Coefficient of variation (CV)4.7523813
Kurtosis71.377259
Mean21.974989
Median Absolute Deviation (MAD)27.88
Skewness-4.7418341
Sum3966903
Variance10906.361
MonotonicityNot monotonic
2025-05-05T23:18:16.725019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1177
 
0.7%
143.99 199
 
0.1%
72 194
 
0.1%
46.8 188
 
0.1%
24 181
 
0.1%
18 175
 
0.1%
63.7 172
 
0.1%
62.4 168
 
0.1%
12 166
 
0.1%
14.4 166
 
0.1%
Other values (21988) 177733
98.5%
ValueCountFrequency (%)
-4274.98 1
< 0.1%
-3442.5 1
< 0.1%
-3366 1
< 0.1%
-3000 1
< 0.1%
-2592 1
< 0.1%
-2550 1
< 0.1%
-2351.25 1
< 0.1%
-2328 1
< 0.1%
-2280 1
< 0.1%
-2255.25 1
< 0.1%
ValueCountFrequency (%)
911.8 1
< 0.1%
864 1
< 0.1%
721.6 1
< 0.1%
720.3 1
< 0.1%
720 2
< 0.1%
712.95 1
< 0.1%
708.75 1
< 0.1%
705.6 2
< 0.1%
705 1
< 0.1%
698.4 2
< 0.1%

customer_segment
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.3 MiB
Consumer
93504 
Corporate
54789 
Home Office
32226 

Length

Max length11
Median length8
Mean length8.839064
Min length8

Characters and Unicode

Total characters1595619
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConsumer
2nd rowConsumer
3rd rowConsumer
4th rowHome Office
5th rowCorporate

Common Values

ValueCountFrequency (%)
Consumer 93504
51.8%
Corporate 54789
30.4%
Home Office 32226
 
17.9%

Length

2025-05-05T23:18:17.335823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-05T23:18:17.591892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
consumer 93504
44.0%
corporate 54789
25.8%
home 32226
 
15.1%
office 32226
 
15.1%

Most occurring characters

ValueCountFrequency (%)
o 235308
14.7%
e 212745
13.3%
r 203082
12.7%
C 148293
9.3%
m 125730
7.9%
n 93504
 
5.9%
s 93504
 
5.9%
u 93504
 
5.9%
f 64452
 
4.0%
t 54789
 
3.4%
Other values (7) 270708
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1595619
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 235308
14.7%
e 212745
13.3%
r 203082
12.7%
C 148293
9.3%
m 125730
7.9%
n 93504
 
5.9%
s 93504
 
5.9%
u 93504
 
5.9%
f 64452
 
4.0%
t 54789
 
3.4%
Other values (7) 270708
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1595619
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 235308
14.7%
e 212745
13.3%
r 203082
12.7%
C 148293
9.3%
m 125730
7.9%
n 93504
 
5.9%
s 93504
 
5.9%
u 93504
 
5.9%
f 64452
 
4.0%
t 54789
 
3.4%
Other values (7) 270708
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1595619
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 235308
14.7%
e 212745
13.3%
r 203082
12.7%
C 148293
9.3%
m 125730
7.9%
n 93504
 
5.9%
s 93504
 
5.9%
u 93504
 
5.9%
f 64452
 
4.0%
t 54789
 
3.4%
Other values (7) 270708
17.0%
Distinct3596
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size11.3 MiB
2025-05-05T23:18:18.393480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length29
Mean length8.553781
Min length2

Characters and Unicode

Total characters1544120
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)< 0.1%

Sample

1st rowBekasi
2nd rowBikaner
3rd rowBikaner
4th rowTownsville
5th rowTownsville
ValueCountFrequency (%)
san 6508
 
2.9%
city 5626
 
2.5%
de 3211
 
1.4%
los 2435
 
1.1%
santo 2339
 
1.0%
new 2316
 
1.0%
york 2316
 
1.0%
domingo 2239
 
1.0%
angeles 1873
 
0.8%
tegucigalpa 1783
 
0.8%
Other values (3762) 197254
86.6%
2025-05-05T23:18:19.722663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 203359
 
13.2%
e 117976
 
7.6%
n 115253
 
7.5%
o 113469
 
7.3%
i 98628
 
6.4%
r 85703
 
5.6%
l 76707
 
5.0%
u 59359
 
3.8%
t 56384
 
3.7%
s 56123
 
3.6%
Other values (50) 561159
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1544120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 203359
 
13.2%
e 117976
 
7.6%
n 115253
 
7.5%
o 113469
 
7.3%
i 98628
 
6.4%
r 85703
 
5.6%
l 76707
 
5.0%
u 59359
 
3.8%
t 56384
 
3.7%
s 56123
 
3.6%
Other values (50) 561159
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1544120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 203359
 
13.2%
e 117976
 
7.6%
n 115253
 
7.5%
o 113469
 
7.3%
i 98628
 
6.4%
r 85703
 
5.6%
l 76707
 
5.0%
u 59359
 
3.8%
t 56384
 
3.7%
s 56123
 
3.6%
Other values (50) 561159
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1544120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 203359
 
13.2%
e 117976
 
7.6%
n 115253
 
7.5%
o 113469
 
7.3%
i 98628
 
6.4%
r 85703
 
5.6%
l 76707
 
5.0%
u 59359
 
3.8%
t 56384
 
3.7%
s 56123
 
3.6%
Other values (50) 561159
36.3%
Distinct164
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.3 MiB
2025-05-05T23:18:20.439006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length22
Mean length8.7728272
Min length4

Characters and Unicode

Total characters1583662
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowIndonesia
2nd rowIndia
3rd rowIndia
4th rowAustralia
5th rowAustralia
ValueCountFrequency (%)
unidos 24869
 
10.8%
estados 24840
 
10.8%
francia 13222
 
5.7%
mexico 13172
 
5.7%
alemania 9564
 
4.1%
australia 8497
 
3.7%
brasil 7987
 
3.5%
reino 7302
 
3.2%
unido 7302
 
3.2%
china 5758
 
2.5%
Other values (175) 108034
46.9%
2025-05-05T23:18:21.369575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 248092
15.7%
i 173374
 
10.9%
n 123666
 
7.8%
s 117962
 
7.4%
o 110463
 
7.0%
d 83403
 
5.3%
e 67560
 
4.3%
r 66669
 
4.2%
l 62119
 
3.9%
t 50532
 
3.2%
Other values (44) 479822
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1583662
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 248092
15.7%
i 173374
 
10.9%
n 123666
 
7.8%
s 117962
 
7.4%
o 110463
 
7.0%
d 83403
 
5.3%
e 67560
 
4.3%
r 66669
 
4.2%
l 62119
 
3.9%
t 50532
 
3.2%
Other values (44) 479822
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1583662
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 248092
15.7%
i 173374
 
10.9%
n 123666
 
7.8%
s 117962
 
7.4%
o 110463
 
7.0%
d 83403
 
5.3%
e 67560
 
4.3%
r 66669
 
4.2%
l 62119
 
3.9%
t 50532
 
3.2%
Other values (44) 479822
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1583662
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 248092
15.7%
i 173374
 
10.9%
n 123666
 
7.8%
s 117962
 
7.4%
o 110463
 
7.0%
d 83403
 
5.3%
e 67560
 
4.3%
r 66669
 
4.2%
l 62119
 
3.9%
t 50532
 
3.2%
Other values (44) 479822
30.3%

order_region
Categorical

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
Central America
28341 
Western Europe
27109 
South America
14935 
Oceania
10148 
Northern Europe
9792 
Other values (18)
90194 

Length

Max length15
Median length14
Mean length12.535007
Min length6

Characters and Unicode

Total characters2262807
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSoutheast Asia
2nd rowSouth Asia
3rd rowSouth Asia
4th rowOceania
5th rowOceania

Common Values

ValueCountFrequency (%)
Central America 28341
15.7%
Western Europe 27109
15.0%
South America 14935
 
8.3%
Oceania 10148
 
5.6%
Northern Europe 9792
 
5.4%
Southeast Asia 9539
 
5.3%
Southern Europe 9431
 
5.2%
Caribbean 8318
 
4.6%
West of USA 7993
 
4.4%
South Asia 7731
 
4.3%
Other values (13) 47182
26.1%

Length

2025-05-05T23:18:21.746775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
europe 50252
13.9%
america 43276
12.0%
asia 31112
 
8.6%
central 30571
 
8.5%
western 27109
 
7.5%
south 26711
 
7.4%
of 18953
 
5.3%
usa 18953
 
5.3%
west 17698
 
4.9%
africa 11614
 
3.2%
Other values (11) 84317
23.4%

Most occurring characters

ValueCountFrequency (%)
e 267374
 
11.8%
r 221631
 
9.8%
a 185888
 
8.2%
184092
 
8.1%
t 170633
 
7.5%
o 129067
 
5.7%
n 114572
 
5.1%
s 105425
 
4.7%
A 104955
 
4.6%
i 104468
 
4.6%
Other values (16) 674702
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2262807
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 267374
 
11.8%
r 221631
 
9.8%
a 185888
 
8.2%
184092
 
8.1%
t 170633
 
7.5%
o 129067
 
5.7%
n 114572
 
5.1%
s 105425
 
4.7%
A 104955
 
4.6%
i 104468
 
4.6%
Other values (16) 674702
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2262807
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 267374
 
11.8%
r 221631
 
9.8%
a 185888
 
8.2%
184092
 
8.1%
t 170633
 
7.5%
o 129067
 
5.7%
n 114572
 
5.1%
s 105425
 
4.7%
A 104955
 
4.6%
i 104468
 
4.6%
Other values (16) 674702
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2262807
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 267374
 
11.8%
r 221631
 
9.8%
a 185888
 
8.2%
184092
 
8.1%
t 170633
 
7.5%
o 129067
 
5.7%
n 114572
 
5.1%
s 105425
 
4.7%
A 104955
 
4.6%
i 104468
 
4.6%
Other values (16) 674702
29.8%
Distinct1089
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.7 MiB
2025-05-05T23:18:22.266605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length31
Mean length10.868086
Min length3

Characters and Unicode

Total characters1961896
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)< 0.1%

Sample

1st rowJava Occidental
2nd rowRajastan
3rd rowRajastan
4th rowQueensland
5th rowQueensland
ValueCountFrequency (%)
de 9121
 
3.5%
del 8417
 
3.2%
inglaterra 6722
 
2.6%
california 5580
 
2.1%
nueva 5473
 
2.1%
isla 4667
 
1.8%
francia 4580
 
1.7%
san 3965
 
1.5%
sur 3354
 
1.3%
renania 3303
 
1.3%
Other values (1167) 208183
79.0%
2025-05-05T23:18:23.124295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 322907
16.5%
n 145815
 
7.4%
i 143282
 
7.3%
e 134534
 
6.9%
o 119614
 
6.1%
r 117395
 
6.0%
l 103549
 
5.3%
82846
 
4.2%
t 80118
 
4.1%
s 75596
 
3.9%
Other values (49) 636240
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1961896
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 322907
16.5%
n 145815
 
7.4%
i 143282
 
7.3%
e 134534
 
6.9%
o 119614
 
6.1%
r 117395
 
6.0%
l 103549
 
5.3%
82846
 
4.2%
t 80118
 
4.1%
s 75596
 
3.9%
Other values (49) 636240
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1961896
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 322907
16.5%
n 145815
 
7.4%
i 143282
 
7.3%
e 134534
 
6.9%
o 119614
 
6.1%
r 117395
 
6.0%
l 103549
 
5.3%
82846
 
4.2%
t 80118
 
4.1%
s 75596
 
3.9%
Other values (49) 636240
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1961896
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 322907
16.5%
n 145815
 
7.4%
i 143282
 
7.3%
e 134534
 
6.9%
o 119614
 
6.1%
r 117395
 
6.0%
l 103549
 
5.3%
82846
 
4.2%
t 80118
 
4.1%
s 75596
 
3.9%
Other values (49) 636240
32.4%

order_status
Categorical

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.5 MiB
COMPLETE
59491 
PENDING_PAYMENT
39832 
PROCESSING
21902 
PENDING
20227 
CLOSED
19616 
Other values (4)
19451 

Length

Max length15
Median length14
Mean length9.6239676
Min length6

Characters and Unicode

Total characters1737309
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOMPLETE
2nd rowPENDING
3rd rowCLOSED
4th rowCOMPLETE
5th rowPENDING_PAYMENT

Common Values

ValueCountFrequency (%)
COMPLETE 59491
33.0%
PENDING_PAYMENT 39832
22.1%
PROCESSING 21902
 
12.1%
PENDING 20227
 
11.2%
CLOSED 19616
 
10.9%
ON_HOLD 9804
 
5.4%
SUSPECTED_FRAUD 4062
 
2.3%
CANCELED 3692
 
2.0%
PAYMENT_REVIEW 1893
 
1.0%

Length

2025-05-05T23:18:23.360274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-05T23:18:23.566291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
complete 59491
33.0%
pending_payment 39832
22.1%
processing 21902
 
12.1%
pending 20227
 
11.2%
closed 19616
 
10.9%
on_hold 9804
 
5.4%
suspected_fraud 4062
 
2.3%
canceled 3692
 
2.0%
payment_review 1893
 
1.0%

Most occurring characters

ValueCountFrequency (%)
E 281578
16.2%
N 197241
11.4%
P 187239
10.8%
O 120617
 
6.9%
C 112455
 
6.5%
T 105278
 
6.1%
D 101295
 
5.8%
M 101216
 
5.8%
L 92603
 
5.3%
I 83854
 
4.8%
Other values (11) 353933
20.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1737309
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 281578
16.2%
N 197241
11.4%
P 187239
10.8%
O 120617
 
6.9%
C 112455
 
6.5%
T 105278
 
6.1%
D 101295
 
5.8%
M 101216
 
5.8%
L 92603
 
5.3%
I 83854
 
4.8%
Other values (11) 353933
20.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1737309
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 281578
16.2%
N 197241
11.4%
P 187239
10.8%
O 120617
 
6.9%
C 112455
 
6.5%
T 105278
 
6.1%
D 101295
 
5.8%
M 101216
 
5.8%
L 92603
 
5.3%
I 83854
 
4.8%
Other values (11) 353933
20.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1737309
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 281578
16.2%
N 197241
11.4%
P 187239
10.8%
O 120617
 
6.9%
C 112455
 
6.5%
T 105278
 
6.1%
D 101295
 
5.8%
M 101216
 
5.8%
L 92603
 
5.3%
I 83854
 
4.8%
Other values (11) 353933
20.4%

order_item_qty
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
1
99134 
5
20385 
3
20350 
4
20335 
2
20315 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180519
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 99134
54.9%
5 20385
 
11.3%
3 20350
 
11.3%
4 20335
 
11.3%
2 20315
 
11.3%

Length

2025-05-05T23:18:23.812383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-05T23:18:24.073137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 99134
54.9%
5 20385
 
11.3%
3 20350
 
11.3%
4 20335
 
11.3%
2 20315
 
11.3%

Most occurring characters

ValueCountFrequency (%)
1 99134
54.9%
5 20385
 
11.3%
3 20350
 
11.3%
4 20335
 
11.3%
2 20315
 
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180519
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 99134
54.9%
5 20385
 
11.3%
3 20350
 
11.3%
4 20335
 
11.3%
2 20315
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180519
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 99134
54.9%
5 20385
 
11.3%
3 20350
 
11.3%
4 20335
 
11.3%
2 20315
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180519
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 99134
54.9%
5 20385
 
11.3%
3 20350
 
11.3%
4 20335
 
11.3%
2 20315
 
11.3%

discount
Real number (ℝ)

High correlation  Zeros 

Distinct1017
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.664741
Minimum0
Maximum500
Zeros10028
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-05-05T23:18:24.319397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.4
median14
Q329.99
95-th percentile62.5
Maximum500
Range500
Interquartile range (IQR)24.59

Descriptive statistics

Standard deviation21.800901
Coefficient of variation (CV)1.0549806
Kurtosis25.231267
Mean20.664741
Median Absolute Deviation (MAD)10
Skewness3.0397955
Sum3730378.4
Variance475.27928
MonotonicityNot monotonic
2025-05-05T23:18:24.567950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10028
 
5.6%
6 4589
 
2.5%
12 4067
 
2.3%
4 3647
 
2.0%
8 3626
 
2.0%
10 3424
 
1.9%
36 3268
 
1.8%
30 3230
 
1.8%
20 3123
 
1.7%
9 2964
 
1.6%
Other values (1007) 138553
76.8%
ValueCountFrequency (%)
0 10028
5.6%
0.1 3
 
< 0.1%
0.11 15
 
< 0.1%
0.12 29
 
< 0.1%
0.15 7
 
< 0.1%
0.16 7
 
< 0.1%
0.18 3
 
< 0.1%
0.2 16
 
< 0.1%
0.22 6
 
< 0.1%
0.23 44
 
< 0.1%
ValueCountFrequency (%)
500 1
 
< 0.1%
400 1
 
< 0.1%
375 25
< 0.1%
360 1
 
< 0.1%
340 1
 
< 0.1%
320 1
 
< 0.1%
300 26
< 0.1%
270 25
< 0.1%
260 1
 
< 0.1%
255 25
< 0.1%

discount_rate
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10166819
Minimum0
Maximum0.25
Zeros10028
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-05-05T23:18:24.772877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.04
median0.1
Q30.16
95-th percentile0.25
Maximum0.25
Range0.25
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.070415215
Coefficient of variation (CV)0.69259829
Kurtosis-0.90115683
Mean0.10166819
Median Absolute Deviation (MAD)0.06
Skewness0.3409276
Sum18353.04
Variance0.0049583024
MonotonicityNot monotonic
2025-05-05T23:18:24.967301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.04 10029
 
5.6%
0.15 10029
 
5.6%
0.25 10029
 
5.6%
0.2 10029
 
5.6%
0.18 10029
 
5.6%
0.17 10029
 
5.6%
0.05 10029
 
5.6%
0.16 10029
 
5.6%
0.13 10029
 
5.6%
0.12 10029
 
5.6%
Other values (8) 80229
44.4%
ValueCountFrequency (%)
0 10028
5.6%
0.01 10028
5.6%
0.02 10028
5.6%
0.03 10029
5.6%
0.04 10029
5.6%
0.05 10029
5.6%
0.06 10029
5.6%
0.07 10029
5.6%
0.09 10029
5.6%
0.1 10029
5.6%
ValueCountFrequency (%)
0.25 10029
5.6%
0.2 10029
5.6%
0.18 10029
5.6%
0.17 10029
5.6%
0.16 10029
5.6%
0.15 10029
5.6%
0.13 10029
5.6%
0.12 10029
5.6%
0.1 10029
5.6%
0.09 10029
5.6%

product_id
Real number (ℝ)

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.851451
Minimum2
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-05-05T23:18:25.241885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q118
median29
Q345
95-th percentile48
Maximum76
Range74
Interquartile range (IQR)27

Descriptive statistics

Standard deviation15.640064
Coefficient of variation (CV)0.49103145
Kurtosis-0.60326101
Mean31.851451
Median Absolute Deviation (MAD)14
Skewness0.3616248
Sum5749792
Variance244.6116
MonotonicityNot monotonic
2025-05-05T23:18:25.473071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 24551
13.6%
18 22246
12.3%
24 21035
11.7%
46 19298
10.7%
45 17325
9.6%
48 15540
8.6%
43 13729
7.6%
9 12487
6.9%
29 10984
6.1%
37 2029
 
1.1%
Other values (41) 21295
11.8%
ValueCountFrequency (%)
2 138
 
0.1%
3 632
 
0.4%
4 67
 
< 0.1%
5 343
 
0.2%
6 328
 
0.2%
7 614
 
0.3%
9 12487
6.9%
10 111
 
0.1%
11 309
 
0.2%
12 423
 
0.2%
ValueCountFrequency (%)
76 650
0.4%
75 838
0.5%
74 529
0.3%
73 357
0.2%
72 492
0.3%
71 434
0.2%
70 208
 
0.1%
69 362
0.2%
68 484
0.3%
67 483
0.3%
Distinct118
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.9 MiB
2025-05-05T23:18:25.797483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length43
Mean length35.110177
Min length4

Characters and Unicode

Total characters6338054
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSmart watch
2nd rowSmart watch
3rd rowSmart watch
4th rowSmart watch
5th rowSmart watch
ValueCountFrequency (%)
men's 77602
 
7.3%
nike 57309
 
5.4%
perfect 49030
 
4.6%
golf 26281
 
2.5%
rip 24515
 
2.3%
deck 24515
 
2.3%
fitness 24515
 
2.3%
cleat 22813
 
2.1%
2 22574
 
2.1%
elite 22309
 
2.1%
Other values (351) 715106
67.0%
2025-05-05T23:18:26.324254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
886050
 
14.0%
e 733504
 
11.6%
i 378177
 
6.0%
n 350567
 
5.5%
r 340664
 
5.4%
o 293733
 
4.6%
t 274503
 
4.3%
s 263028
 
4.1%
l 230952
 
3.6%
a 230847
 
3.6%
Other values (56) 2356029
37.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6338054
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
886050
 
14.0%
e 733504
 
11.6%
i 378177
 
6.0%
n 350567
 
5.5%
r 340664
 
5.4%
o 293733
 
4.6%
t 274503
 
4.3%
s 263028
 
4.1%
l 230952
 
3.6%
a 230847
 
3.6%
Other values (56) 2356029
37.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6338054
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
886050
 
14.0%
e 733504
 
11.6%
i 378177
 
6.0%
n 350567
 
5.5%
r 340664
 
5.4%
o 293733
 
4.6%
t 274503
 
4.3%
s 263028
 
4.1%
l 230952
 
3.6%
a 230847
 
3.6%
Other values (56) 2356029
37.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6338054
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
886050
 
14.0%
e 733504
 
11.6%
i 378177
 
6.0%
n 350567
 
5.5%
r 340664
 
5.4%
o 293733
 
4.6%
t 274503
 
4.3%
s 263028
 
4.1%
l 230952
 
3.6%
a 230847
 
3.6%
Other values (56) 2356029
37.2%

product_price
Real number (ℝ)

High correlation 

Distinct75
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.23255
Minimum9.99
Maximum1999.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-05-05T23:18:26.560739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.99
5-th percentile31.99
Q150
median59.99
Q3199.99
95-th percentile399.98
Maximum1999.99
Range1990
Interquartile range (IQR)149.99

Descriptive statistics

Standard deviation139.73249
Coefficient of variation (CV)0.98937881
Kurtosis23.313
Mean141.23255
Median Absolute Deviation (MAD)40
Skewness3.1910197
Sum25495158
Variance19525.168
MonotonicityNot monotonic
2025-05-05T23:18:26.819612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.99 24820
13.7%
129.99 22372
12.4%
50 21035
11.7%
49.98 19298
10.7%
399.98 17325
9.6%
199.99 15622
8.7%
299.98 13729
7.6%
99.99 12433
6.9%
39.99 11201
6.2%
24.99 2339
 
1.3%
Other values (65) 20345
11.3%
ValueCountFrequency (%)
9.99 285
 
0.2%
11.29 271
 
0.2%
11.54 529
 
0.3%
14.99 593
 
0.3%
15.99 602
 
0.3%
17.99 298
 
0.2%
19.99 887
 
0.5%
21.99 295
 
0.2%
22 308
 
0.2%
24.99 2339
1.3%
ValueCountFrequency (%)
1999.99 15
 
< 0.1%
1500 442
 
0.2%
999.99 10
 
< 0.1%
599.99 21
 
< 0.1%
532.58 484
 
0.3%
461.48 484
 
0.3%
452.04 592
 
0.3%
399.99 67
 
< 0.1%
399.98 17325
9.6%
357.1 652
 
0.4%

Interactions

2025-05-05T23:18:04.885772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:50.814920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:52.818446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:54.909032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:57.130135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:59.092178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:01.025155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:02.934873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:05.130123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:51.114969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:53.075140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:55.162908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:57.362983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:59.326990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:01.267815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:03.163158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:05.397901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:51.373120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:53.337137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:55.431075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:57.629642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:59.581270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:01.519348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:03.397131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:05.648318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:51.632750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:53.614073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:55.696949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:57.883557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:59.815863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:01.771641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:03.638432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:05.877553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:51.866341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:53.864616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:55.948081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:58.116976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:00.041250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:02.003682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:03.861711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:06.112706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:52.096638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:54.126902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:56.325484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:58.357102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:00.265390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:02.233731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:04.079857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:06.348568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:52.326832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:54.374965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:56.570885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:58.586214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:00.505572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:02.449514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:04.375835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:06.606421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:52.569011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:54.646325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:56.833741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:17:58.827421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:00.759099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:02.686975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-05T23:18:04.628313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-05-05T23:18:27.000750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
customer_segmentdelivery_statusdiscountdiscount_ratelate_delivery_riskorder_item_qtyorder_regionorder_statusproduct_idproduct_priceprofit_per_ordersales_actualsales_originshipping_days_actualshipping_days_scheduledshipping_mode
customer_segment1.0000.0050.0020.0020.0000.0040.0170.0110.0080.0020.0000.0060.0040.0090.0080.008
delivery_status0.0051.0000.0000.0031.0000.0000.0180.5770.0000.0000.0000.0050.0000.5610.3210.321
discount0.0020.0001.0000.7950.0000.0820.0180.0000.0730.3560.1960.4200.5340.0010.0000.000
discount_rate0.0020.0030.7951.0000.0050.0000.0000.0030.0000.000-0.054-0.1330.0000.0010.0000.000
late_delivery_risk0.0001.0000.0000.0051.0000.0000.0170.2340.0000.0040.0000.0000.0020.6310.4570.457
order_item_qty0.0040.0000.0820.0000.0001.0000.0310.0000.2320.2290.0340.2120.2350.0000.0020.002
order_region0.0170.0180.0180.0000.0170.0311.0000.0170.1180.0600.0140.0260.0420.0220.0230.023
order_status0.0110.5770.0000.0030.2340.0000.0171.0000.0010.0030.0000.0060.0040.0080.0090.009
product_id0.0080.0000.0730.0000.0000.2320.1180.0011.0000.1950.0610.1620.178-0.0020.0090.009
product_price0.0020.0000.3560.0000.0040.2290.0600.0030.1951.0000.2870.6390.667-0.0000.0080.008
profit_per_order0.0000.0000.196-0.0540.0000.0340.0140.0000.0610.2871.0000.4410.436-0.0030.0020.002
sales_actual0.0060.0050.420-0.1330.0000.2120.0260.0060.1620.6390.4411.0000.986-0.0000.0040.004
sales_origin0.0040.0000.5340.0000.0020.2350.0420.0040.1780.6670.4360.9861.000-0.0000.0060.006
shipping_days_actual0.0090.5610.0010.0010.6310.0000.0220.008-0.002-0.000-0.003-0.000-0.0001.0000.6810.681
shipping_days_scheduled0.0080.3210.0000.0000.4570.0020.0230.0090.0090.0080.0020.0040.0060.6811.0001.000
shipping_mode0.0080.3210.0000.0000.4570.0020.0230.0090.0090.0080.0020.0040.0060.6811.0001.000

Missing values

2025-05-05T23:18:07.274809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-05T23:18:08.294412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

order_dateshipping_dateshipping_modedelivery_statusshipping_days_actualshipping_days_scheduledlate_delivery_risksales_originsales_actualprofit_per_ordercustomer_segmentorder_cityorder_countryorder_regionorder_stateorder_statusorder_item_qtydiscountdiscount_rateproduct_idproduct_nameproduct_price
02018-01-312018-02-03Standard ClassAdvance shipping340327.75314.6400191.25ConsumerBekasiIndonesiaSoutheast AsiaJava OccidentalCOMPLETE113.110.0473Smart watch327.75
12018-01-132018-01-18Standard ClassLate delivery541327.75311.35999-249.09ConsumerBikanerIndiaSouth AsiaRajastanPENDING116.390.0573Smart watch327.75
22018-01-132018-01-17Standard ClassShipping on time440327.75309.72000-247.78ConsumerBikanerIndiaSouth AsiaRajastanCLOSED118.030.0673Smart watch327.75
32018-01-132018-01-16Standard ClassAdvance shipping340327.75304.8100022.86Home OfficeTownsvilleAustraliaOceaniaQueenslandCOMPLETE122.940.0773Smart watch327.75
42018-01-132018-01-15Standard ClassAdvance shipping240327.75298.25000134.21CorporateTownsvilleAustraliaOceaniaQueenslandPENDING_PAYMENT129.500.0973Smart watch327.75
52018-01-132018-01-19Standard ClassShipping canceled640327.75294.9800118.58ConsumerToowoombaAustraliaOceaniaQueenslandCANCELED132.780.1073Smart watch327.75
62018-01-132018-01-15First ClassLate delivery211327.75288.4200195.18Home OfficeGuangzhouChinaEastern AsiaGuangdongCOMPLETE139.330.1273Smart watch327.75
72018-01-132018-01-15First ClassLate delivery211327.75285.1400168.43CorporateGuangzhouChinaEastern AsiaGuangdongPROCESSING142.610.1373Smart watch327.75
82018-01-132018-01-16Second ClassLate delivery321327.75278.59000133.72CorporateGuangzhouChinaEastern AsiaGuangdongCLOSED149.160.1573Smart watch327.75
92018-01-132018-01-15First ClassLate delivery211327.75275.31000132.15CorporateGuangzhouChinaEastern AsiaGuangdongCLOSED152.440.1673Smart watch327.75
order_dateshipping_dateshipping_modedelivery_statusshipping_days_actualshipping_days_scheduledlate_delivery_risksales_originsales_actualprofit_per_ordercustomer_segmentorder_cityorder_countryorder_regionorder_stateorder_statusorder_item_qtydiscountdiscount_rateproduct_idproduct_nameproduct_price
1805092016-01-162016-01-19Standard ClassAdvance shipping340399.98001335.980010.00CorporateGuangshuiChinaEastern AsiaHubeiPENDING_PAYMENT164.00.1645Field & Stream Sportsman 16 Gun Fire Safe399.98
1805102016-01-162016-01-19Standard ClassAdvance shipping340399.98001331.98001165.99CorporateGuangshuiChinaEastern AsiaHubeiPENDING_PAYMENT168.00.1745Field & Stream Sportsman 16 Gun Fire Safe399.98
1805112016-01-162016-01-18Second ClassShipping on time220399.98001327.98001157.43ConsumerChengduChinaEastern AsiaSichuanON_HOLD172.00.1845Field & Stream Sportsman 16 Gun Fire Safe399.98
1805122016-01-162016-01-22Standard ClassLate delivery641399.98001319.9800186.40Home OfficeChengduChinaEastern AsiaSichuanCOMPLETE180.00.2045Field & Stream Sportsman 16 Gun Fire Safe399.98
1805132016-01-162016-01-19Standard ClassAdvance shipping340399.98001299.98999119.99Home OfficeShanghaiChinaEastern AsiaShanghaiPENDING_PAYMENT1100.00.2545Field & Stream Sportsman 16 Gun Fire Safe399.98
1805142016-01-162016-01-20Standard ClassShipping on time440399.98001399.9800140.00Home OfficeShanghaiChinaEastern AsiaShanghaiCLOSED10.00.0045Field & Stream Sportsman 16 Gun Fire Safe399.98
1805152016-01-162016-01-19Second ClassLate delivery321399.98001395.98001-613.77CorporateHirakataJaponEastern AsiaOsakaCOMPLETE14.00.0145Field & Stream Sportsman 16 Gun Fire Safe399.98
1805162016-01-152016-01-20Standard ClassLate delivery541399.98001391.98001141.11CorporateAdelaideAustraliaOceaniaAustralia del SurPENDING18.00.0245Field & Stream Sportsman 16 Gun Fire Safe399.98
1805172016-01-152016-01-18Standard ClassAdvance shipping340399.98001387.98001186.23ConsumerAdelaideAustraliaOceaniaAustralia del SurPENDING_PAYMENT112.00.0345Field & Stream Sportsman 16 Gun Fire Safe399.98
1805182016-01-152016-01-19Standard ClassShipping on time440399.98001383.98001168.95ConsumerNagercoilIndiaSouth AsiaTamil NaduPENDING_PAYMENT116.00.0445Field & Stream Sportsman 16 Gun Fire Safe399.98